AI Payroll Data Validation Automation

Every month we upload thousands of payroll lines from Excel/CSV files, direct HR software exports and the occasional online form or email attachment. Right now a team member opens each file, spots obvious issues, runs manual look-ups against the previous period and then applies every client-specific rule before anything even hits our payroll engine. I want that entire front-end check replaced by an AI layer so the team can spend their time on advisory conversations instead of mechanical validation.

Scope
First task is to design, build and deploy an automated checking service that plugs into our existing third-party payroll platform. The service must:

• Flag missing, invalid or duplicate employee records in real time
• Compare current data with the prior payroll to surface unusual spikes or drops in pay elements
• Apply each client’s bespoke rules (hours caps, allowance limits, cost-centre logic, etc.) and highlight exceptions before the run proceeds

Ideal flow: data lands in a monitored folder or API, the AI engine scores each row, produces an exception report and feeds a clean file straight into our payroll software. A simple dashboard or email summary showing pass/fail statistics will let the processor decide whether to move forward or investigate.

Deliverables
1. Current-state process map and tech stack review
2. Working AI validation engine (code, model and configuration files) connected to our test payroll environment
3. Exception reporting interface plus user guide
4. Deployment playbook so we can roll the solution to additional clients

Acceptance criteria will be sign-off of a pilot pay run where the engine catches ≥95 % of historically logged errors with